Using early extremes to place the 2022 UK heat waves into historical context

Yule, Emma L.; Hegerl, Gabriele C.; Schurer, Andrew; Hawkins, Ed

doi:10.1002/asl.1159

Cited by 15 publications

(15 citation statements)

References 32 publications

(43 reference statements)

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“…This is particularly vital for climate data, where digitized historical weather records offer invaluable insights into past climatic changes and trends, aiding in refining current climate models and forecasts [5,6]. Moreover, digitization addresses the gaps in climate records, facilitating more comprehensive and efficient modeling and analysis, and reinforcing the critical convergence of historical authenticity and technological advancement [6,7].…”

Section: Introductionmentioning

confidence: 99%

Data Rescue for Historical Document Tables Using Semi-Supervised Learning

Singh,

Middleton

2024

Preprint

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This study uses a novel semi-supervised learning framework to explore Tabular Structure Recognition (TSR) for digitizing historical documents, specifically employing the CascadeTabNet model. TSR is crucial for transforming archival tabular data into digital formats, enhancing accessibility and analysis across various research fields. Challenges like physical degradation, inconsistent lighting, and non-standard handwriting hinder the generation of high-quality annotations of historical documents needed for effective model training. To address these issues, this research explores two research questions: (i) Can a semi-supervised training approach reduce the need for expensive data annotations? and (ii) Does semi-supervised training improve model robustness? We applied our methodology across three datasets: the GloSAT and ICDAR-2019 datasets based on historical documents, and the predominantly modern documents PubTabNet dataset. Our results indicate that semi-supervised learning substantially increases TSR accuracy and decreases dependency on extensive labelled datasets, providing a robust solution for large-scale digitization initiatives and contributing to the preservation and improved accessibility of historical data. All code from this paper is freely available on GitHub.

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Section: Introductionmentioning

confidence: 99%

Data Rescue for Historical Document Tables Using Semi-Supervised Learning

Singh,

Middleton

2024

Preprint

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“…Over the past few decades, the number of heat waves has increased (e.g., Frich et al, 2002;Christidis et al, 2015;Zhang et al, 2020). The years 2003The years , 2010The years , 2018The years , 2022The years , and 2023 were among the hottest in Europe, characterized by record-breaking near-surface air temperatures (e.g., Stott et al, 2004;Barriopedro et al, 2011;Dirmeyer et al, 2021;Yule et al, 2023;Jiang et al, 2024;Lemus-Canovas et al, 2024). With projected climate change, the occurrence of heat waves will continue to increase (e.g., Russo et al, 2015;Hari et al, 2020;Molina et al, 2020;Masson-Delmotte et al, 2021), leading to multiple negative socio-economic impacts (e.g., Amengual et al, 2014;Yin et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Impact of groundwater representation on heat events in regional climate simulations over Europe

Poshyvailo-Strube,

Wagner,

Goergen

et al. 2024

Earth Syst. Dynam.

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Abstract. The representation of groundwater is simplified in most regional climate models (RCMs), potentially leading to biases in the simulations. This study introduces a unique dataset from the regional Terrestrial Systems Modelling Platform (TSMP) driven by the Max Planck Institute Earth System Model at Low Resolution (MPI-ESM-LR) boundary conditions in the context of dynamical downscaling of global climate models (GCMs) for climate change studies. TSMP explicitly simulates full 3D soil and groundwater dynamics together with overland flow, including complete water and energy cycles from the bedrock to the top of the atmosphere. By comparing the statistics of heat events, i.e., a series of consecutive days with a near-surface temperature exceeding the 90th percentile of the reference period, from TSMP and those from GCM–RCM simulations with simplified groundwater dynamics from the COordinated Regional Climate Downscaling EXperiment (CORDEX) for the European domain, we aim to improve the understanding of how groundwater representation affects heat events in Europe. The analysis was carried out using RCM outputs for the summer seasons of 1976–2005 relative to the reference period of 1961–1990. While our results show that TSMP simulates heat events consistently with the CORDEX ensemble, there are some systematic differences that we attribute to the more realistic representation of groundwater in TSMP. Compared to the CORDEX ensemble, TSMP simulates fewer hot days (i.e., days with a near-surface temperature exceeding the 90th percentile of the reference period) and lower interannual variability and decadal change in the number of hot days on average over Europe. TSMP systematically simulates fewer heat waves (i.e., heat events lasting 6 d or more) compared to the CORDEX ensemble; moreover, they are shorter and less intense. The Iberian Peninsula is particularly sensitive with respect to groundwater. Therefore, incorporating an explicit 3D groundwater representation in RCMs may be a key in reducing biases in simulated duration, intensity, and frequency of heat waves in Europe. The results highlight the importance of hydrological processes for the long-term regional climate simulations and provide indications of possible potential implications for climate change projections.

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“…These implications are usually unequal, with disproportionate impacts on vulnerable populations who contribute the least to the issue, which exacerbates social inequalities (Islam & Winkel, 2017). In extreme event risk management such as urban flood defense and extreme weather warning, there is a tendency to prioritize preventing and adapting strategies based on the highest anomalies in the observed or historical archives, so record‐shattering extreme events often result in significant damage (Fischer et al., 2021); for example, the unprecedented flood that occurred in Zhengzhou, China, in 2021 disrupted the livelihoods of 3.98 million people and killed 16 (Guo et al., 2023), the record‐breaking heatwave that occurred in the UK in 2022 killed 3,200 people (Yule et al., 2023), and very extreme climate anomalies that occurred in Europe in 2003 led to persistent droughts and HW resulting in at least 35,000 deaths (Ciais et al., 2005). Trends in vulnerability and exposure are the main drivers of change in disaster risk and of impacts when risk is realized (Field, 2012).…”

Section: Introductionmentioning

confidence: 99%

Future Global Population Exposure to Record‐Breaking Climate Extremes

Li,

Liu,

Wang

et al. 2023

Earth's Future

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The increase in record‐breaking extreme events caused by climate change poses a threat to human health and well‐being. Understanding the future impacts of such events on global populations can provide decision‐making support for policies aiming to mitigate climate change. Here, we investigated the population exposure to eight climate extreme indices and drivers of exposure trajectories based on National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections Coupled Model Intercomparison Project 6 and population projection data under four shared socioeconomic pathway scenarios at a spatial resolution of 0.25° × 0.25°. The results show that by the mid‐twenty‐first century, most regions worldwide, especially Africa and South America, will continue to experience record‐breaking temperatures and compound drought and heatwaves (CDHWs). Regarding population exposure, under SSP3‐7.0 in the late twenty‐first century, the mean value of the multimodel median expected annual exposure (EAE) of all extreme temperature indices and CDHW reaches 8.12 billion persons per year. Population exposure hotspots will be concentrated in Central Africa, South Asia, Southeast Asia, and East Asia, mostly in developing countries, where 55.01%–87.42% of the EAE is found. The drivers of exposure trajectories are spatially heterogeneous. The increase in record‐breaking probability contributes more than population growth to EAE growth in most regions of the world except Central Asia, the Middle East, and most of Africa. These findings reveal the future trajectories of record‐breaking probabilities and population exposures for climate extremes, which can inform understanding of the intersections between climate change and population change and future risk management.

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Using early extremes to place the 2022 UK heat waves into historical context

Cited by 15 publications

References 32 publications

Data Rescue for Historical Document Tables Using Semi-Supervised Learning

Data Rescue for Historical Document Tables Using Semi-Supervised Learning

Impact of groundwater representation on heat events in regional climate simulations over Europe

Future Global Population Exposure to Record‐Breaking Climate Extremes

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